I am fairly new to BERT, and I am willing to test two approaches to get "the most similar words" to a given word to use in Snorkel labeling functions for weak supervision. Fist approach was to use word2vec with pre-trained word embedding of "word2vec-google-news-300" to find the most similar words @labeling_function() def lf_find_good_synonyms(x): good_synonyms = word_vectors.most_similar("good", topn=25) ##Similar words are extracted here good_list = syn_list(good_synonyms) ##syn_list just returns the stemmed similar word return POSITIVE if any(word in x.stemmed for …

I am trying to label a span recognition dataset using Snorkel and am currently at the stage of improving labeling functions. One of the LF has a rather low coverage because it only labels a subclass of one of the entity spans. What would be the impact of low coverage labeling functions on the final downstream span recognition model?